Tech stack

Responsibilities

• Partner with the Data Science teams to harden experimental code and take it from a sandbox environment into production by applying engineering best practices
• Design, implement and own scalable ML infrastructure and deployment pipelines capable of handling high-volume model training and inference workloads
• Build and maintain automated CI/CD pipelines for ML model development, testing, validation, and deployment, integrating with customer platforms and Databricks environments
• Define, monitor and continuously improve KPIs covering model performance, data quality, system reliability, deployment velocity, and operational efficiency
• Establish and implement MLOps best practices including experiment tracking, model versioning, feature stores, and governance (e.g. MLflow, Unity Catalog)
• Optimize ML infrastructure for cost efficiency and performance through automated scaling and resource management

Requirements

• Master’s degree in computer science, Machine Learning, Data Engineering, or a related field
• 2+ years of professional experience in ML Engineering, MLOps, or DevOps with a strong focus on production ML systems
• Strong Python programming skills and proficiency with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience across the full ML lifecycle: experiment tracking (e.g. MLFlow), model deployment, and production operations
• Hands-on experience with ML workflow orchestration and pipeline automation
• Experience deploying and operating ML systems preferably on cloud platforms (Azure preferred; AWS or GCP also valued)
• Strong problem-solving skills and ability to work independently in fast-paced environments
• Fluency in English, both written and spoken

Nice to have

• PhD degree in Computer Science, Data Engineering, AI, or a related field (completed or in progress)
• Hands-on experience deploying and managing ML models in Databricks environments
• Experience with containerized ML workloads using Docker and Kubernetes
• Experience implementing model monitoring, observability, and performance tracking
• Knowledge of feature stores and model versioning best practices

ML / MLOps Engineer

Application

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HR & technical interview

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Meeting with the Manager

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Offer

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Feedback

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